域自适应的mamba用于跨场景的高光谱图像分类
概括
这项研究引入了一种新型的域适应Mamba (DAMamba),用于高效的跨场景高光谱图像分类. 通过对准细粒度特征和减轻光谱转移,DAMamba提高了准确性并减少了计算时间.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 跨场景的高光谱图像分类面临挑战,目标域样本有限.
- 现有的方法经常对齐全球特征,忽视细微细节和高效建模.
- 变压器架构虽然对依赖性有效,但却存在二次复杂性问题.
研究的目的:
- 为改进跨场景的高光谱图像分类提出一种新的域适应Mamba (DAMamba).
- 解决现有方法在细粒度特征对齐和计算效率方面的局限性.
- 为了提高分类准确性,并减少处理时间在无监督域调整场景.
主要方法:
- 开发了一种光谱空间Mamba用于高阶语义特征提取.
- 引入了一个域不变原型对齐方法 (域内,域间,小批) 用于伪标签生成和光谱转移缓解.
- 利用一个完全连接的层来对对齐的目标域特征进行最终分类.
主要成果:
- 在不同的跨场景数据集中,DAMamba表现出卓越的分类准确性.
- 与现有方法相比,提出的方法显著提高了计算效率.
- 实现了对光谱转移的有效缓解和可靠的伪标签生成.
结论:
- DAMamba提供了一个更有效,更准确的解决方案,用于跨场景的高光谱图像分类.
- 细粒度对齐和光谱转移缓解策略对于域调整至关重要.
- 拟议的方法推进了超光谱成像无监督域适应的最先进技术.
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